Unlock the power of data with our Data Science course at Desert Side Training Institute. In this course, you’ll delve into the world of data analysis, machine learning, and artificial intelligence, gaining the skills needed to extract valuable insights and make informed decisions. Whether you’re a beginner or looking to advance your career in data science, our comprehensive curriculum covers everything from data wrangling and visualization to predictive modeling and deep learning. Join us and take your first step towards becoming a data-driven professional.

Data Science Syllabus
50-hour plan

  • MODULE 1
    – Getting into Data Analytics, Platforms to use
    – Data cleaning using Pandas
    – EDA using Pandas
    – Data Visualization using Matplotlib
    – Data Visualizing using Seaborn
    – Data Visualizing using Plotly
    – Web scraping using Python
  • MODULE 2
    – Introduction to databases and big query setup
    – Basic queries in SQL
    – Filters and Functions
    – Joins
    – Group by & Aggregation
    – Window functions
    – Date and Time Functions
    – Subqueries and Views
  • MODULE 3
    – Overview of Concepts of Statistics
    – Population and Sampling Technologies
    – Measures of Central Tendency
    – Distributions
    – Standardization and Normalisation
    – Probability
    – Permutations and Combinations
    – Hypothesis test, A/B test
  • MODULE 4
    – Introduction to spreadsheets
    – Excel Formulas
    – Pivot tables, Charts
  • MODULE 5
    – Introduction to AI vs ML vs DL, Public data domains
    – Different kinds of ML problems and The life cycle of a machine learning problem
    – Supervised Learning – Linear regression- mathematical intuition
    – Linear regression project
    – Evaluation metrics
    – Logistic regression – mathematical intuition
    – Logistic regression project
    – Classification evaluation metrics
    – SVM
    – Naive Bayes
    – Decision trees
    – KNN
    – Bagging and Boosting Techniques – Random Forest
    – AdaBoost
    – XGBoost
    – Unsupervised Learning – k-means Clustering
    – Elbow and Silhouette method
    – Hierarchical clustering
    – Gaussian mixture models
    – Recommender systems
    – Time Series Analysis

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